gvc_agora_opentargets

Setup environment

library(tidyverse)
library(janitor)
library(broom)
library(readxl)
library(jsonlite)

library(gprofiler2)

theme_set(theme_bw())

set.seed(666)

Read and prep data

GVC

Genes within 1Mb window of (each side of?) GVC loci from Fanny:

gvc <- read_xlsx("GVC_1Mb_comparison_050224.xlsx") %>%
  clean_names() %>% 
  separate(gene_id, c("gene_id", "version")) %>%
  select(-version, -agora_nominated_list, -opentarget_info)

gvc

gvc.genes <- gvc %>% distinct(gene_id, .keep_all = TRUE) %>% select(gene_id, gene_symbol) %>% arrange(gene_symbol)

gvc.genes

Agora

Alzheimer’s disease gene prioritization scores from Agora (see also related journal article):

ago1 <- read_json("syn25741025.overall_scores.json", simplifyVector = TRUE) %>% as_tibble()

ago1

Alzheimer’s disease genes (AMPAD Agora) from Fanny:

ago2 <- read_csv("AMPAD_agora_032124_gene-list.csv")
ago2

ago <- ago1 %>% filter(hgnc_symbol %in% ago2$`Gene Symbol`)

OpenTargets

Alzheimer’s disease gene prioritization scores from OpenTargets:

# ot <- read_tsv("OT-MONDO_0004975-associated-targets-6_4_2024-v24_03.tsv", show_col_types = FALSE, na = "No data")

ot <- read_tsv("OT-MONDO_0004975-associated-targets-9_19_2024-v24_09.tsv", show_col_types = FALSE, na = "No data")

ot

Add Ensembl Gene IDs (WTF!):

otcols <- colnames(ot)
otensg <- gconvert(
  query = ot$symbol,
  organism = "hsapiens",
  target= "ENSG",
  mthreshold = Inf,
  filter_na = TRUE) %>% 
  mutate(input_number = as.character(input_number)) %>%
  left_join(ot %>% rownames_to_column(var = "input_number"), by = "input_number") %>% 
  select(ensembl_gene_id = target, otcols)

otensg

Overlaps between GVC, Agora, and OpenTargets genes

x = list(
  "GVC" = gvc.genes$gene_id,
  "Agora" = ago$ensembl_gene_id,
  "OpenTargets" = otensg$ensembl_gene_id
)

library(VennDiagram)
grid.newpage()
v <- venn.diagram(
  x,
  fill = c("#FF0000", "#00FF00", "#0000FF"),
  filename = NULL)
grid.draw(v)

p <- get.venn.partitions(x)
p

Perform ORA of genes in overlaps

GVC ∩ Agora ∩ OpenTargets

genes <- p %>% 
  filter(..set.. == "GVC∩Agora∩OpenTargets") %>%
  unnest(..values..) %>%
  select(gene_id = ..values..) %>%
  left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>% 
  left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>%
  sample_frac(1L) %>% # randomize row order before arranging
  select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
  arrange(desc(genetics_score), desc(otGeneticsPortal))

genes

query <- genes %>% distinct(gene_id) %>% pull(gene_id)

gostres <- gost(query = query,
                organism = "hsapiens",
                domain_scope = "annotated",
                exclude_iea = TRUE,
                ordered_query = TRUE,
                significant = TRUE,
                user_threshold = 0.005,
                correction_method = "fdr")

gostres$result %>% select(term_name, term_id, source, everything())

gostplot(gostres, capped = FALSE, interactive = TRUE)

# save overlap gene ids for later
overlap_gene_ids <- query

GVC ∩ Agora

genes <- p %>% 
  filter(..set.. %in% c("GVC∩Agora∩OpenTargets", "(GVC∩Agora)∖(OpenTargets)")) %>%
  unnest(..values..) %>%
  select(gene_id = ..values..) %>%
  left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>% 
  left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>% 
  sample_frac(1L) %>% # randomize row order before arranging
  select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
  arrange(desc(genetics_score), desc(otGeneticsPortal))

genes

query <- genes %>% distinct(gene_id) %>% pull(gene_id)

gostres <- gost(query = query,
                organism = "hsapiens",
                domain_scope = "annotated",
                exclude_iea = TRUE,
                ordered_query = TRUE,
                significant = TRUE,
                user_threshold = 0.005,
                correction_method = "fdr")

gostres$result %>% select(term_name, term_id, source, everything())

gostplot(gostres, capped = FALSE, interactive = TRUE)

GVC ∩ OpenTargets

genes <- p %>% 
  filter(..set.. %in% c("GVC∩Agora∩OpenTargets", "(GVC∩OpenTargets)∖(Agora)")) %>%
  unnest(..values..) %>%
  select(gene_id = ..values..) %>%
  left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>% 
  left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>% 
  sample_frac(1L) %>% # randomize row order before arranging
  select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
  arrange(desc(genetics_score), desc(otGeneticsPortal))

genes

query <- genes %>% distinct(gene_id) %>% pull(gene_id)

gostres <- gost(query = query,
                organism = "hsapiens",
                domain_scope = "annotated",
                exclude_iea = TRUE,
                ordered_query = TRUE,
                significant = TRUE,
                user_threshold = 0.005,
                correction_method = "fdr")

gostres$result %>% select(term_name, term_id, source, everything())

gostplot(gostres, capped = FALSE, interactive = TRUE)

Agora ∩ OpenTargets

genes <- p %>% 
  filter(..set.. %in% c("GVC∩Agora∩OpenTargets", "(Agora∩OpenTargets)∖(GVC)")) %>%
  unnest(..values..) %>%
  select(gene_id = ..values..) %>%
  left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>% 
  left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>% 
  sample_frac(1L) %>% # randomize row order before arranging
  select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
  arrange(desc(genetics_score), desc(otGeneticsPortal))

genes

query <- genes %>% distinct(gene_id) %>% pull(gene_id)

gostres <- gost(query = query,
                organism = "hsapiens",
                domain_scope = "annotated",
                exclude_iea = TRUE,
                ordered_query = TRUE,
                significant = TRUE,
                user_threshold = 0.005,
                correction_method = "fdr")

gostres$result %>% select(term_name, term_id, source, everything())

gostplot(gostres, capped = FALSE, interactive = TRUE)

(GVC ∩ Agora) ∪ (GVC ∩ OpenTargets) ∪ (Agora ∩ OpenTargets)

genes <- p %>% 
  filter(..set.. %in% c("GVC∩Agora∩OpenTargets", "(GVC∩Agora)∖(OpenTargets)", "(GVC∩OpenTargets)∖(Agora)", "(Agora∩OpenTargets)∖(GVC)")) %>%
  unnest(..values..) %>%
  select(gene_id = ..values..) %>%
  left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>% 
  left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>% 
  sample_frac(1L) %>% # randomize row order before arranging
  select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
  arrange(desc(genetics_score), desc(otGeneticsPortal))

genes

query <- genes %>% distinct(gene_id) %>% pull(gene_id)

gostres <- gost(query = query,
                organism = "hsapiens",
                domain_scope = "annotated",
                exclude_iea = TRUE,
                ordered_query = TRUE,
                significant = TRUE,
                user_threshold = 0.005,
                correction_method = "fdr")

gostres$result %>% select(term_name, term_id, source, everything())

gostplot(gostres, capped = FALSE, interactive = TRUE)

(Agora ∩ OpenTargets) ∖ (GVC)

genes <- p %>%
  filter(..set.. == "(Agora∩OpenTargets)∖(GVC)") %>% 
  unnest(..values..) %>%
  select(gene_id = ..values..) %>%
  left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>% 
  left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>% 
  sample_frac(1L) %>% # randomize row order before arranging
  select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
  arrange(desc(genetics_score), desc(otGeneticsPortal))

genes

query <- genes %>% distinct(gene_id) %>% pull(gene_id)

gostres <- gost(query = query,
                organism = "hsapiens",
                domain_scope = "annotated",
                exclude_iea = TRUE,
                ordered_query = TRUE,
                significant = TRUE,
                user_threshold = 0.005,
                correction_method = "fdr")

gostres$result %>% select(term_name, term_id, source, everything())

gostplot(gostres, capped = FALSE, interactive = TRUE)

(GVC ∩ OpenTargets) ∖ (Agora)

genes <- p %>%
  filter(..set.. == "(GVC∩OpenTargets)∖(Agora)") %>% 
  unnest(..values..) %>%
  select(gene_id = ..values..) %>%
  left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>% 
  left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>% 
  sample_frac(1L) %>% # randomize row order before arranging
  select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
  arrange(desc(genetics_score), desc(otGeneticsPortal))

genes

query <- genes %>% distinct(gene_id) %>% pull(gene_id)

gostres <- gost(query = query,
                organism = "hsapiens",
                domain_scope = "annotated",
                exclude_iea = TRUE,
                ordered_query = TRUE,
                significant = TRUE,
                user_threshold = 0.005,
                correction_method = "fdr")

gostres$result %>% select(term_name, term_id, source, everything())

gostplot(gostres, capped = FALSE, interactive = TRUE)

(OpenTargets) ∖ (GVC ∪ Agora)

genes <- p %>%
  filter(..set.. == "(OpenTargets)∖(GVC∪Agora)") %>% 
  unnest(..values..) %>%
  select(gene_id = ..values..) %>%
  left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>% 
  left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>% 
  sample_frac(1L) %>% # randomize row order before arranging
  select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
  arrange(desc(genetics_score), desc(otGeneticsPortal))

genes

query <- genes %>% distinct(gene_id) %>% pull(gene_id)

gostres <- gost(query = query,
                organism = "hsapiens",
                domain_scope = "annotated",
                exclude_iea = TRUE,
                ordered_query = TRUE,
                significant = TRUE,
                user_threshold = 0.005,
                correction_method = "fdr")

gostres$result %>% select(term_name, term_id, source, everything())

gostplot(gostres, capped = FALSE, interactive = TRUE)

(GVC ∩ Agora) ∖ (OpenTargets)

genes <- p %>%
  filter(..set.. == "(GVC∩Agora)∖(OpenTargets)") %>% 
  unnest(..values..) %>%
  select(gene_id = ..values..) %>%
  left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>% 
  left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>% 
  sample_frac(1L) %>% # randomize row order before arranging
  select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
  arrange(desc(genetics_score), desc(otGeneticsPortal))

genes

query <- genes %>% distinct(gene_id) %>% pull(gene_id)

gostres <- gost(query = query,
                organism = "hsapiens",
                domain_scope = "annotated",
                exclude_iea = TRUE,
                ordered_query = TRUE,
                significant = TRUE,
                user_threshold = 0.005,
                correction_method = "fdr")

gostres$result %>% select(term_name, term_id, source, everything())

gostplot(gostres, capped = FALSE, interactive = TRUE)

(Agora) ∖ (GVC ∪ OpenTargets)

genes <- p %>%
  filter(..set.. == "(Agora)∖(GVC∪OpenTargets)") %>% 
  unnest(..values..) %>%
  select(gene_id = ..values..) %>%
  left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>% 
  left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>% 
  sample_frac(1L) %>% # randomize row order before arranging
  select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
  arrange(desc(genetics_score), desc(otGeneticsPortal))

genes

query <- genes %>% distinct(gene_id) %>% pull(gene_id)

gostres <- gost(query = query,
                organism = "hsapiens",
                domain_scope = "annotated",
                exclude_iea = TRUE,
                ordered_query = TRUE,
                significant = TRUE,
                user_threshold = 0.005,
                correction_method = "fdr")

gostres$result %>% select(term_name, term_id, source, everything())

gostplot(gostres, capped = FALSE, interactive = TRUE)

(GVC) ∖ (Agora ∪ OpenTargets)

genes <- p %>%
  filter(..set.. == "(GVC)∖(Agora∪OpenTargets)") %>% 
  unnest(..values..) %>%
  select(gene_id = ..values..) %>%
  left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>% 
  left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>% 
  sample_frac(1L) %>% # randomize row order before arranging
  select(gene_id, symbol, genetics_score, otGeneticsPortal, globalScore, target_risk_score, multi_omics_score) %>%
  arrange(desc(genetics_score), desc(otGeneticsPortal))

genes

query <- genes %>% distinct(gene_id) %>% pull(gene_id)

gostres <- gost(query = query,
                organism = "hsapiens",
                domain_scope = "annotated",
                exclude_iea = TRUE,
                ordered_query = TRUE,
                significant = TRUE,
                user_threshold = 0.005,
                correction_method = "fdr")

gostres$result %>% select(term_name, term_id, source, everything())

gostplot(gostres, capped = FALSE, interactive = TRUE)

Perform ORA of GVC genes sorted by Agora or OpenTargets scores

GVC genes sorted by Agora’s genetics_score

Arrange by Agora’s genetics_score and OpenTargets’ otGeneticsPortal:

d1 <- gvc.genes %>%
  left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>% 
  left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>% 
  sample_frac(1L) %>% # randomize row order before arranging
  arrange(desc(genetics_score), desc(otGeneticsPortal)) %>% 
  select(-c(symbol, hgnc_symbol)) %>% 
  select(gene_id, gene_symbol, genetics_score, otGeneticsPortal, everything())

d1

query <- d1 %>% distinct(gene_id) %>% pull(gene_id)

gostres <- gost(query = query,
                organism = "hsapiens",
                domain_scope = "annotated",
                exclude_iea = TRUE,
                ordered_query = TRUE,
                significant = TRUE,
                user_threshold = 0.005,
                correction_method = "fdr")

gostres$result %>% select(term_name, term_id, source, everything())

gostplot(gostres, capped = FALSE, interactive = TRUE)

GVC genes sorted by OpenTargets’ otGeneticsPortal

Arrange by OpenTargets’ otGeneticsPortal and Agora’s genetics_score:

d2 <- gvc.genes %>%
  left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>% 
  left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>% 
  sample_frac(1L) %>% # randomize row order before arranging
  arrange(desc(otGeneticsPortal), desc(genetics_score)) %>% 
  select(-c(symbol, hgnc_symbol)) %>% 
  select(gene_id, gene_symbol, otGeneticsPortal, genetics_score, everything())

d2

query <- d2 %>% distinct(gene_id) %>% pull(gene_id)

gostres <- gost(query = query,
                organism = "hsapiens",
                domain_scope = "annotated",
                exclude_iea = TRUE,
                ordered_query = TRUE,
                significant = TRUE,
                user_threshold = 0.005,
                correction_method = "fdr")

gostres$result %>% select(term_name, term_id, source, everything())

gostplot(gostres, capped = FALSE, interactive = TRUE)

GVC genes sorted by Agora’s target_risk_score

Arrange by Agora’s target_risk_score and OpenTargets’ globalScore:

d3 <- gvc.genes %>%
  left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>% 
  left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>% 
  sample_frac(1L) %>% # randomize row order before arranging
  arrange(desc(target_risk_score), desc(globalScore)) %>% 
  select(-c(symbol, hgnc_symbol)) %>% 
  select(gene_id, gene_symbol, target_risk_score, globalScore, everything())

d3

query <- d3 %>% distinct(gene_id) %>% pull(gene_id)

gostres <- gost(query = query,
                organism = "hsapiens",
                domain_scope = "annotated",
                exclude_iea = TRUE,
                ordered_query = TRUE,
                significant = TRUE,
                user_threshold = 0.005,
                correction_method = "fdr")

gostres$result %>% select(term_name, term_id, source, everything())

gostplot(gostres, capped = FALSE, interactive = TRUE)

GVC genes sorted by OpenTargets’ globalScore

Arrange by OpenTargets’ globalScore and Agora’s target_risk_score:

d4 <- gvc.genes %>%
  left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>% 
  left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>% 
  sample_frac(1L) %>% # randomize row order before arranging
  arrange(desc(globalScore), desc(target_risk_score)) %>% 
  select(-c(symbol, hgnc_symbol)) %>% 
  select(gene_id, gene_symbol, globalScore, target_risk_score, everything())

d4

query <- d4 %>% distinct(gene_id) %>% pull(gene_id)

gostres <- gost(query = query,
                organism = "hsapiens",
                domain_scope = "annotated",
                exclude_iea = TRUE,
                ordered_query = TRUE,
                significant = TRUE,
                user_threshold = 0.005,
                correction_method = "fdr")

gostres$result %>% select(term_name, term_id, source, everything())

gostplot(gostres, capped = FALSE, interactive = TRUE)

Correlation of Agora and OpenTargets scores (GVC genes only)

d.cor <- gvc.genes %>%
  left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>% 
  left_join(otensg, by = join_by(gene_id == ensembl_gene_id))

d.cor %>% nrow()

[1] 1347

d.cor %>% drop_na(genetics_score, otGeneticsPortal) %>% nrow()

[1] 56

d.cor %>% drop_na(genetics_score, otGeneticsPortal) %>%
  summarize(cor = tidy(cor.test(genetics_score, otGeneticsPortal, method="kendall"))) %>%
  unnest(cor)

d.cor %>% nrow()

[1] 1347

d.cor %>% drop_na(target_risk_score, globalScore) %>% nrow()

[1] 75

d.cor %>% drop_na(target_risk_score, globalScore) %>%
  summarize(cor = tidy(cor.test(target_risk_score, globalScore, method="kendall"))) %>%
  unnest(cor)

Correlation of Agora and OpenTargets scores (all genes)

d.cor <- ago %>% 
  left_join(otensg, by = "ensembl_gene_id")

d.cor %>% nrow()

[1] 925

d.cor %>% drop_na(genetics_score, otGeneticsPortal) %>% nrow()

[1] 75

d.cor %>% drop_na(genetics_score, otGeneticsPortal) %>%
  summarize(cor = tidy(cor.test(genetics_score, otGeneticsPortal, method="kendall"))) %>%
  unnest(cor)

d.cor %>% nrow()

[1] 925

d.cor %>% drop_na(target_risk_score, globalScore) %>% nrow()

[1] 488

d.cor %>% drop_na(target_risk_score, globalScore) %>%
  summarize(cor = tidy(cor.test(target_risk_score, globalScore, method="kendall"))) %>%
  unnest(cor)

GVC loci annotated with genes in overlaps

GVC ∩ Agora ∩ OpenTargets

gene_ids <- p %>% 
  filter(..set.. == "GVC∩Agora∩OpenTargets") %>%
  unnest(..values..) %>%
  select(gene_id = ..values..) %>%
  left_join(ago, by = join_by(gene_id == ensembl_gene_id)) %>% 
  left_join(otensg, by = join_by(gene_id == ensembl_gene_id)) %>%
  distinct(gene_id) %>%
  pull(gene_id)

length(gene_ids)

[1] 75

gvc %>%
  filter(gene_id %in% gene_ids) %>%
  select(gvc_locus = grouped_loci_gvc, gene_id, gene_symbol) %>%
  arrange(gene_symbol) %>%
  mutate(gene = gene_symbol) %>%
  # unite(gene, gene_id, gene_symbol, sep = ":", remove = FALSE) %>%
  distinct(gvc_locus, gene, .keep_all = TRUE) %>%
  group_by(gvc_locus) %>%
  summarize(genes = str_c(gene, collapse = " | ")) %>%
  select(gvc_locus, genes) %>%
  gt::gt()

gvc_locus	genes
ABCA7	ABCA7 | NDUFS7
ABI3 / ACE	NGFR | ZNF652
ACE	ACE
ADAM10 / MINDY2	ADAM10 | ALDH1A2 | LIPC
ADAMTS4	ADAMTS4 | FCER1G | NDUFS2
ANK3 / CCDC6	CCDC6 | SLC16A9
ANKRD31	ANKRD31 | ENC1
APH1B	LACTB
APOE / TOMM40	APOC1 | APOE | BCAM | MARK4 | NECTIN2
APP	MRPL39
APP / ADAMTS1	ADAMTS1
BCKDK / KAT8 / VKORC1	BCKDK | STX4 | VKORC1
BIN1	BIN1
CASS4	CASS4
CD2AP	CD2AP
CD33	CD33
CHRNE	ENO3 | RABEP1 | SLC25A11 | ZFP3
CLU / PTK2B	CLU | EPHX2 | PTK2B | SCARA3
CR1	CR1
CTSH	CTSH
DOC2A	DOC2A
ECHDC3 / USP6NL	USP6NL
EED / PICALM	DLG2 | PICALM
EPHA1 / EPHA1-AS1	EPHA1
HAVCR2	CYFIP2 | HAVCR2
HLA	HLA-DRA | HLA-DRB1
ICA1	NXPH1
IDUA	CPLX1
IL34	MTSS2
INPP5D	INPP5D
LILRB2 / TMC4	LAIR1
MADD / SPI1	C1QTNF4 | NDUFS3 | NR1H3 | RAPSN | SPI1
MS4A / MS4A2 / MS4A4A / MS4A6A	MRPL16 | MS4A2 | MS4A4A | MS4A6A
NDUFAF7 / PRKD3	QPCT
NYAP1 / PILRA / SPDYE3 / ZCWPW1	NYAP1
OARD1 / TREM2 / TREML2 / UNC5CL	TREM2
PLCG2	PLCG2 | SDR42E1
PLEKHA1	HTRA1
RABEP1 / SCIMP	ENO3 | RABEP1 | SLC25A11 | ZFP3
RASGEF1C	MAPK9
RIN3 / SLC24A4	RIN3 | SLC24A4
SHARPIN	PLEC
SIGLEC11	NR1H2
WNT3	NSF

Perform ORA of Agora and OpenTargets genes sorted by global or genetic score

Agora genes sorted by genetics_score

d5 <- ago %>%
  drop_na(genetics_score) %>%
  arrange(desc(genetics_score))

d5

query <- d5 %>% distinct(ensembl_gene_id) %>% pull(ensembl_gene_id)

gostres <- gost(query = query,
                organism = "hsapiens",
                domain_scope = "annotated",
                exclude_iea = TRUE,
                ordered_query = TRUE,
                significant = TRUE,
                user_threshold = 0.005,
                correction_method = "fdr")

gostres$result %>% select(term_name, term_id, source, everything())

gostplot(gostres, capped = FALSE, interactive = TRUE)

OpenTargets genes sorted by otGeneticsPortal

d6 <- otensg %>%
  drop_na(otGeneticsPortal) %>%
  arrange(desc(otGeneticsPortal))

d6

query <- d6 %>% distinct(ensembl_gene_id) %>% pull(ensembl_gene_id)

gostres <- gost(query = query,
                organism = "hsapiens",
                domain_scope = "annotated",
                exclude_iea = TRUE,
                ordered_query = TRUE,
                significant = TRUE,
                user_threshold = 0.005,
                correction_method = "fdr")

gostres$result %>% select(term_name, term_id, source, everything())

gostplot(gostres, capped = FALSE, interactive = TRUE)

Agora genes sorted by target_risk_score

d7 <- ago %>%
  drop_na(target_risk_score) %>%
  arrange(desc(target_risk_score))

d7

query <- d7 %>% distinct(ensembl_gene_id) %>% pull(ensembl_gene_id)

gostres <- gost(query = query,
                organism = "hsapiens",
                domain_scope = "annotated",
                exclude_iea = TRUE,
                ordered_query = TRUE,
                significant = TRUE,
                user_threshold = 0.005,
                correction_method = "fdr")

gostres$result %>% select(term_name, term_id, source, everything())

gostplot(gostres, capped = FALSE, interactive = TRUE)

OpenTargets genes sorted by globalScore

d8 <- otensg %>%
  drop_na(globalScore) %>%
  arrange(desc(globalScore))

d8

query <- d8 %>% distinct(ensembl_gene_id) %>% pull(ensembl_gene_id)

gostres <- gost(query = query,
                organism = "hsapiens",
                domain_scope = "annotated",
                exclude_iea = TRUE,
                ordered_query = TRUE,
                significant = TRUE,
                user_threshold = 0.005,
                correction_method = "fdr")

gostres$result %>% select(term_name, term_id, source, everything())

gostplot(gostres, capped = FALSE, interactive = TRUE)

Check missing OpenTargets scores in Brian’s table

t <- read_xlsx("8-23-2024 - GVC Table 1C - WORKING COPYL_MRC.xlsx", skip = 1, na = "No data") |> janitor::clean_names() |> select(gvc_expanded_list_of_possible_genes_500kb, open_target_scores_global, open_target_scores_genetics) |> rename(symbol = gvc_expanded_list_of_possible_genes_500kb)

t |>
  left_join(ot, by = "symbol") |>
  filter(round(open_target_scores_global, 4) != round(globalScore, 4)) |> 
  select(symbol, open_target_scores_global, globalScore)

t |>
  left_join(ot, by = "symbol") |>
  filter(round(open_target_scores_genetics, 4) != round(otGeneticsPortal, 4)) |> 
  select(symbol, open_target_scores_genetics, otGeneticsPortal)

Print environment

sessioninfo::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14)
 os       macOS Sonoma 14.6.1
 system   aarch64, darwin20
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/New_York
 date     2024-09-19
 pandoc   3.1.11 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/aarch64/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package        * version   date (UTC) lib source
 backports        1.5.0     2024-05-23 [1] CRAN (R 4.4.0)
 bit              4.0.5     2022-11-15 [1] CRAN (R 4.4.0)
 bit64            4.0.5     2020-08-30 [1] CRAN (R 4.4.0)
 bitops           1.0-8     2024-07-29 [1] CRAN (R 4.4.0)
 broom          * 1.0.6     2024-05-17 [1] CRAN (R 4.4.0)
 cellranger       1.1.0     2016-07-27 [1] CRAN (R 4.4.0)
 cli              3.6.3     2024-06-21 [1] CRAN (R 4.4.0)
 colorspace       2.1-1     2024-07-26 [1] CRAN (R 4.4.0)
 crayon           1.5.3     2024-06-20 [1] CRAN (R 4.4.0)
 crosstalk        1.2.1     2023-11-23 [1] CRAN (R 4.4.0)
 data.table       1.16.0    2024-08-27 [1] CRAN (R 4.4.1)
 digest           0.6.37    2024-08-19 [1] CRAN (R 4.4.1)
 dplyr          * 1.1.4     2023-11-17 [1] CRAN (R 4.4.0)
 evaluate         0.24.0    2024-06-10 [1] CRAN (R 4.4.0)
 fansi            1.0.6     2023-12-08 [1] CRAN (R 4.4.0)
 fastmap          1.2.0     2024-05-15 [1] CRAN (R 4.4.0)
 forcats        * 1.0.0     2023-01-29 [1] CRAN (R 4.4.0)
 formatR          1.14      2023-01-17 [1] CRAN (R 4.4.0)
 futile.logger  * 1.4.3     2016-07-10 [1] CRAN (R 4.4.0)
 futile.options   1.0.1     2018-04-20 [1] CRAN (R 4.4.0)
 generics         0.1.3     2022-07-05 [1] CRAN (R 4.4.0)
 ggplot2        * 3.5.1     2024-04-23 [1] CRAN (R 4.4.0)
 glue             1.7.0     2024-01-09 [1] CRAN (R 4.4.0)
 gprofiler2     * 0.2.3     2024-02-23 [1] CRAN (R 4.4.0)
 gt               0.11.0    2024-07-09 [1] CRAN (R 4.4.0)
 gtable           0.3.5     2024-04-22 [1] CRAN (R 4.4.0)
 hms              1.1.3     2023-03-21 [1] CRAN (R 4.4.0)
 htmltools        0.5.8.1   2024-04-04 [1] CRAN (R 4.4.0)
 htmlwidgets      1.6.4     2023-12-06 [1] CRAN (R 4.4.0)
 httpuv           1.6.15    2024-03-26 [1] CRAN (R 4.4.0)
 httr             1.4.7     2023-08-15 [1] CRAN (R 4.4.0)
 janitor        * 2.2.0     2023-02-02 [1] CRAN (R 4.4.0)
 jsonlite       * 1.8.8     2023-12-04 [1] CRAN (R 4.4.0)
 knitr            1.48      2024-07-07 [1] CRAN (R 4.4.0)
 labeling         0.4.3     2023-08-29 [1] CRAN (R 4.4.0)
 lambda.r         1.2.4     2019-09-18 [1] CRAN (R 4.4.0)
 later            1.3.2     2023-12-06 [1] CRAN (R 4.4.0)
 lazyeval         0.2.2     2019-03-15 [1] CRAN (R 4.4.0)
 lifecycle        1.0.4     2023-11-07 [1] CRAN (R 4.4.0)
 lubridate      * 1.9.3     2023-09-27 [1] CRAN (R 4.4.0)
 magrittr         2.0.3     2022-03-30 [1] CRAN (R 4.4.0)
 mime             0.12      2021-09-28 [1] CRAN (R 4.4.0)
 munsell          0.5.1     2024-04-01 [1] CRAN (R 4.4.0)
 pillar           1.9.0     2023-03-22 [1] CRAN (R 4.4.0)
 pkgconfig        2.0.3     2019-09-22 [1] CRAN (R 4.4.0)
 plotly           4.10.4    2024-01-13 [1] CRAN (R 4.4.0)
 promises         1.3.0     2024-04-05 [1] CRAN (R 4.4.0)
 purrr          * 1.0.2     2023-08-10 [1] CRAN (R 4.4.0)
 R6               2.5.1     2021-08-19 [1] CRAN (R 4.4.0)
 Rcpp             1.0.13    2024-07-17 [1] CRAN (R 4.4.0)
 RCurl            1.98-1.16 2024-07-11 [1] CRAN (R 4.4.0)
 readr          * 2.1.5     2024-01-10 [1] CRAN (R 4.4.0)
 readxl         * 1.4.3     2023-07-06 [1] CRAN (R 4.4.0)
 rlang            1.1.4     2024-06-04 [1] CRAN (R 4.4.0)
 rmarkdown        2.28      2024-08-17 [1] CRAN (R 4.4.0)
 rstudioapi       0.16.0    2024-03-24 [1] CRAN (R 4.4.0)
 sass             0.4.9     2024-03-15 [1] CRAN (R 4.4.0)
 scales           1.3.0     2023-11-28 [1] CRAN (R 4.4.0)
 sessioninfo      1.2.2     2021-12-06 [1] CRAN (R 4.4.0)
 shiny            1.9.1     2024-08-01 [1] CRAN (R 4.4.0)
 snakecase        0.11.1    2023-08-27 [1] CRAN (R 4.4.0)
 stringi          1.8.4     2024-05-06 [1] CRAN (R 4.4.0)
 stringr        * 1.5.1     2023-11-14 [1] CRAN (R 4.4.0)
 tibble         * 3.2.1     2023-03-20 [1] CRAN (R 4.4.0)
 tidyr          * 1.3.1     2024-01-24 [1] CRAN (R 4.4.0)
 tidyselect       1.2.1     2024-03-11 [1] CRAN (R 4.4.0)
 tidyverse      * 2.0.0     2023-02-22 [1] CRAN (R 4.4.0)
 timechange       0.3.0     2024-01-18 [1] CRAN (R 4.4.0)
 tzdb             0.4.0     2023-05-12 [1] CRAN (R 4.4.0)
 utf8             1.2.4     2023-10-22 [1] CRAN (R 4.4.0)
 vctrs            0.6.5     2023-12-01 [1] CRAN (R 4.4.0)
 VennDiagram    * 1.7.3     2022-04-12 [1] CRAN (R 4.4.0)
 viridisLite      0.4.2     2023-05-02 [1] CRAN (R 4.4.0)
 vroom            1.6.5     2023-12-05 [1] CRAN (R 4.4.0)
 withr            3.0.1     2024-07-31 [1] CRAN (R 4.4.0)
 xfun             0.47      2024-08-17 [1] CRAN (R 4.4.0)
 xml2             1.3.6     2023-12-04 [1] CRAN (R 4.4.0)
 xtable           1.8-4     2019-04-21 [1] CRAN (R 4.4.0)
 yaml             2.3.10    2024-07-26 [1] CRAN (R 4.4.0)

 [1] /Users/marcoe02/.Rlib
 [2] /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library

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